Strong performance – Models have been employed in the Lynx Program for many years and have generated solidly positive results
Diversified – Trades approximately 90 markets using signals generated by a diverse group of models spanning multiple timeframes and machine learning concepts
Adaptable and able to learn – Models employ machine learning techniques to learn and capitalize from complex linear and nonlinear relationships
Investing in funds is associated with risk. Past performance is no guarantee of future return. The numbers shown above should be read together with this clarifying note.
Average annual return
Average annual return
Lynx Constellation employs systematic trading models utilizing a range of machine learning techniques to statistically forecast market prices. The models attempt to identify both linear and non-linear relationships between past and future prices across a broadly diversified portfolio of fixed income, stock indices, foreign exchange and commodities. Generally, Lynx machine learning models are equipped to identify and exploit complex non-linear imbalances in macro driven markets. Some of these relationships are based on investor tendencies and behavioral biases such as herding, while others are based on repeating patterns of price action influenced by other markets and/or factors such as seasonality. As the models constantly adapt to – and learn from – new information, the market phenomenon exploited at any given time will change with the environment.
Risk management and risk considerations
Risk management in Lynx Constellation is based on three pillars: model-driven risk control, robust portfolio construction and a top-down risk limit framework. The models incorporate risk management elements into the signal generation process, increasing or decreasing risk based on size constraints and volatility. During the portfolio construction process, the optimizer considers volatility and correlation between markets when determining position sizes and total portfolio risk. Finally, we utilize a risk limit framework on a market and portfolio level, employing three separate models in parallel based on Value at Risk to measure market risk over different time frames; the model stating the highest measure at any given time is used to limit risk. Investing in funds is associated with risk. Past performance is no guarantee of future return. The value of the capital invested in the fund may increase or decrease and investors cannot be certain of recovering all of their invested capital.